Validation and comparison of GC-MS, FT-MIR, and FT-NIR techniques for rapid bromoform quantification in Asparagopsis taxiformis extracts

Bromoform-rich extracts of Asparagopsis taxiformis represent a promising sustainable strategy for mitigating methane emissions in ruminants. Accurate quantification of bromoform is essential to ensure both efficacy and safety. Although gas chromatography-mass spectrometry (GC-MS) offers high accuracy, it is time-consuming, resource-intensive, and requires significant chemical reagents. This study pioneers the use of Fourier-transform near-infrared (FT-NIR), Fourier-transform mid-infrared (FT-MIR), and their data fusion (FT-MIR-NIR) combined with a recursive weighted partial least squares (rPLS) variable selection algorithm for rapid, non-destructive quantification of bromoform in seaweed extracts, validated against GC-MS. The partial least squares regression (PLSR) models employing rPLS based on FT-MIR spectra (R²CV = 0.95, RMSECV = 3.59 ppm (µL/L)) and fused FT-MIR-NIR spectra (R²CV = 0.94, RMSECV = 3.90 ppm) demonstrated robust predictive performance for bromoform quantification, though slightly lower than GC-MS (R²CV = 0.99, RMSECV = 3.21 ppm). This work establishes a high-throughput, accurate, and efficient alternative method for bromoform analysis, aligning with green chemistry principles by reducing chemical usage and enhancing sustainability. The approach holds significant potential for applications in animal nutrition and industrial feed processing. Future studies should further explore this method for bromoform quantification in other seaweed extracts and for high-throughput screening in animal feed industrial processes.